app.py

import gradio as gr
import numpy as np
import random
from PIL import Image
import os

import spaces
from diffusers import StableDiffusion3Pipeline
import torch
from peft import PeftModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "frankjoshua/stable-diffusion-3.5-medium"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = StableDiffusion3Pipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

lora_models = {
    "None": None,
    "GenEval": "jieliu/SD3.5M-FlowGRPO-GenEval",
    "Text Rendering": "jieliu/SD3.5M-FlowGRPO-Text",
    "Human Prefer": "jieliu/SD3.5M-FlowGRPO-PickScore",
}

lora_prompts = {
    "GenEval": os.path.join(os.getcwd(), "prompts/geneval.txt"),
    "Text Rendering": os.path.join(os.getcwd(), "prompts/ocr.txt"),
    "Human Prefer": os.path.join(os.getcwd(), "prompts/pickscore.txt"),
}

pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_models["GenEval"], adapter_name="GenEval")
pipe.transformer.load_adapter(lora_models["Text Rendering"], adapter_name="Text Rendering")
pipe.transformer.load_adapter(lora_models["Human Prefer"], adapter_name="Human Prefer")
pipe = pipe.to(device)

# COUNTER_FILE = os.path.join(os.getcwd(),"model_call_counter.txt")
COUNTER_FILE = os.path.join("/data/model_call_counter.txt")

def get_call_count():
    if not os.path.exists(COUNTER_FILE):
        return 0
    try:
        with open(COUNTER_FILE, 'r') as f:
            return int(f.read().strip())
    except:
        return 0

def update_call_count():
    count = get_call_count() + 1
    with open(COUNTER_FILE, 'w') as f:
        f.write(str(count))
    return count

def sample_prompt(lora_model):
    if lora_model in lora_models and lora_model != "None":
        file_path = f"{lora_prompts[lora_model]}"
        try:
            with open(file_path, 'r') as file:
                prompts = file.readlines()
                if lora_model=='GenEval':
                    total_lines = len(prompts)
                    if total_lines > 0:
                        weights = [1/(i+1) for i in range(total_lines)]
                        sum_weights = sum(weights)
                        normalized_weights = [w/sum_weights for w in weights]
                        return random.choices(prompts, weights=normalized_weights, k=1)[0].strip()
                    return "No prompts found in file."
                else:
                    return random.choice(prompts).strip()
        except FileNotFoundError:
            return "Prompt file not found."
    return ""

def create_grid_image(images):
    # Create a 2x2 grid from the 4 images
    width, height = images[0].size
    grid_image = Image.new('RGB', (width * 2, height * 2))
    
    # Paste images in a 2x2 grid
    grid_image.paste(images[0], (0, 0))
    grid_image.paste(images[1], (width, 0))
    grid_image.paste(images[2], (0, height))
    grid_image.paste(images[3], (width, height))
    
    return grid_image

@spaces.GPU
def infer(
    prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    lora_model,
    progress=gr.Progress(track_tqdm=True),
):
    call_count = update_call_count()
    
    images = []
    seeds = []
    
    # Generate 4 images
    for i in range(4):
        if randomize_seed:
            current_seed = random.randint(0, MAX_SEED)
        else:
            current_seed = seed + i  # Use sequential seeds if not randomizing
        
        seeds.append(current_seed)
        generator = torch.Generator().manual_seed(current_seed)
        sampled_prompt = sample_prompt(lora_model)
        final_prompt = prompt if prompt else sampled_prompt

        if lora_model == "None":
            with pipe.transformer.disable_adapter():
                image = pipe(
                    prompt=final_prompt,
                    negative_prompt="",
                    guidance_scale=guidance_scale,
                    num_inference_steps=num_inference_steps,
                    width=width,
                    height=height,
                    generator=generator,
                ).images[0]
        else:
            pipe.transformer.set_adapter(lora_model)
            image = pipe(
                prompt=final_prompt,
                negative_prompt="",
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                width=width,
                height=height,
                generator=generator,
            ).images[0]
        
        images.append(image)
    
    # Create a 2x2 grid from the 4 images
    grid_image = create_grid_image(images)
    
    return grid_image, ", ".join(map(str, seeds)), f"Model has been called {call_count} times"


css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
# SD3.5 Medium + Flow-GRPO

Our model is trained separately for different tasks, so it’s best to use the corresponding prompt format for each task.

**User Guide:**
1. Select a LoRA model (choose “None” to use the base model)  
2. Click “Sample Prompt” to randomly select from ~1000 task-specific prompts, or write your own  
3. Click “Run” to generate images (a 2×2 grid of 4 images will be produced)

**Note:**  
- For the *Text Rendering* task, please enclose the text to be displayed in **double quotes (`"`)**, not single quotes (`'`)  
""")
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

        with gr.Row():
            lora_model = gr.Dropdown(
                label="LoRA Model",
                choices=list(lora_models.keys()),
                value="GenEval"
            )

            sample_prompt_button = gr.Button("Sample Prompt", scale=0, variant="secondary")

            def update_sampled_prompt(lora_model):
                return sample_prompt(lora_model)

            sample_prompt_button.click(
                fn=update_sampled_prompt,
                inputs=[lora_model],
                outputs=[prompt]
            )

            run_button = gr.Button("Run", scale=0, variant="primary")
        
        
        result = gr.Image(label="Results (2x2 Grid)", show_label=True)
        seed_display = gr.Textbox(label="Seeds Used", show_label=True)
        
        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Starting Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seeds", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=4.5,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=40,  # Replace with defaults that work for your model
                )

        call_count_display = gr.Textbox(
            label="Model Call Count", 
            value=f"Model has been called {get_call_count()} times",
            interactive=False
        )
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            lora_model,
        ],
        outputs=[result, seed_display, call_count_display],
    )

if __name__ == "__main__":
    demo.launch()